A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Abstract

This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80 percent were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.

Assessing Pollution Risk in Ardabil Aquifer Groundwater of Iran with Arsenic and Nitrate Using the SINTACS Model

“…The areas with moderate and high risk included the western and northern parts of the study area. Considering the land use map, we found that these areas are agricultural lands where the fertilizers containing nitrate are used and can be considered as the main source of nitrate pollution, which is consistent with the results of Sajedi-Hosseini et al [26].…”

“…The permitted rate of nitrate in groundwater is 50 mg/L [24,25]. Due to the importance of nitrate in groundwater, in many studies nitrate concentration has been used to validate models that examine vulnerability or pollution risk of groundwater [26,27]. Therefore, to validate the SINTACS model, nitrate concentration in groundwater was used.…”

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

“…Validation performance is a critical step in a modeling procedure, for which several statistical indices has been suggested and used [13,14,[49][50][51][52]. In this study, we used Area Under Receiver Operating Characteristic (ROC) curve (AUC) [39,[53][54][55][56], Root Mean Squared Error (RMSE) [57][58][59][60][61][62][63][64], Kappa, Accuracy (ACC), Specificity (SPF), Sensitivity (SST), Negative predictive value (NPV), and Positive predictive value (PPV) [65][66][67][68][69].…”

Flood Prediction Using Machine Learning Models: Literature Review

“…Ensemble ML methods have a long tradition in flood prediction. In recent years, ensemble prediction systems (EPSs) [141] were proposed as efficient prediction systems to provide an ensemble of N forecasts. In EPS, N is the number of independent realizations of a model probability distribution.…”

“…Ouyang et al [145] and Zhang et al [146] presented a review of the applications of ensemble ML methods used for floods. EPSs were demonstrated to have the capability for improving model accuracy in flood modeling [140][141][142][143][144][145][146] To improve the accuracy of import data and to achieve better dataset management, the ensemble mean was proposed as a powerful approach coupled with ML methods [140,141]. Empirical mode decomposition (EMD) [142], and ensemble EMD (EEMD) [143] are widely used for flood prediction [144].…”